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| Analisi di sensibilità per bias nascosto (Limiti di Rosenbaum / E-value)× | Aggiustamento Frontdoor (Criterio Frontdoor)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2002 | 1995 |
| Ideatore≠ | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) | Judea Pearl |
| Tipo≠ | Sensitivity analysis for causal inference | Causal identification (graphical adjustment) |
| Fonte seminale≠ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 | Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688. DOI ↗ |
| Alias≠ | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity | frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment) |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). | Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured. |
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